Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- Oak Ridge National Laboratory
- Pennsylvania State University
- Cornell University
- Duke University
- Harvard University
- Human Technopole
- INSERM
- Imperial College London
- Indiana University
- Inria, the French national research institute for the digital sciences
- KU LEUVEN
- New York University
- SUNY University at Buffalo
- SciLifeLab
- Umeå University
- University of Connecticut
- University of Turku
- University of Virginia
- University of Washington
- 10 more »
- « less
-
Field
-
modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
-
research questions and investigate trends in outcomes among people with diabetes. Advanced epidemiological and statistical methods will be applied, including causal inference approaches such as target trial
-
via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
-
. Preferred qualifications: Strong methodological training in statistical inference, computational statistics, reinforcement learning, or optimization. Experience with Bayesian statistics, adaptive clinical
-
-experiments for research purposes, Knowledge of online experiment, task and/or survey platforms (e.g. Gorilla, Prolific, etc.), Awareness of time-series, multilevel, Bayesian, or causal inference analysis
-
Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. 5. Y. Ban, X. Alameda-Pineda, L. Girin, and R. Horaud, "Variational Bayesian inference for audio-visual tracking of multiple speakers," IEEE TPAMI, 2019. 6. X. Alameda-Pineda et al., "Socially
-
groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
-
theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
-
that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
-
, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015). 3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and